This blog examines past, current, and best practices, techniques, and lessons learned of various business intelligence implementations.

Metadata Management

May 20, 2016

Within seconds, a company executive in the U.S. can know exactly how many parts their global manufacturing plants are producing. A delivery company can tell you exactly to the minute when their truck will be arriving. A utility company can monitor usage across the country and know when it’s reaching a peak. All of this can be done because of the Internet of Things (IoT) and Big Data.

The IoT is basically a collection of Internet - enabled devices or sensors, other than your computer, which are connected to the Internet and can send and receive data. Big data is what you get when all of this information is collected and analyzed.

Devices such as smartphones, scanners, sensors, and GPS can gather and distribute a lot of information. IoT technology allows the input from these devices to be pulled together. Once it's all been collected, companies can utilize big data analytics tools to improve business operations, manage equipment and people, target marketing and make their business run more effectively and efficiently.

The IoT is forcing people and companies to change the way they look at things. Information is being funneled fast, in large amounts, structured and unstructured and from places we never thought we’d get information from. Refrigerators talking to smartphones for a shopping list, or fitness trackers measuring your burned calories, sensors sending vital health data to doctors to monitor their patient‘s health in real time, and anything else you can imagine. Vendors can then use that information for marketing directly to consumers or provide better and timely service. Inventory in stores could soon be reliant on just a sensor on a shelf that indicates when an item needs to be restocked.

The next step for businesses is to figure out how to make the most of the data pouring in from things like smart meters, devices, and sensors. How is this data going to affect your next business decision and how is it all going to be analyzed?

Companies need to plan for a continuing influx of data as more devices become connected and interconnected. You need the bandwidth to store data, the real-time analytical tools to analyze it, and the ability to monetize it and turn it into something profitable. Without a plan, you could be left behind.

October 23, 2009

At the heart of every successful master data management (MDM)
strategy is master data that is complete and accurate at all times. But, the
optimum quality and consistency of master data can only be secured if
comprehensive data governance plays an integral role in its creation,
collection, storage, handling, and administration.

The Data Governance Institute, a provider of in-depth,
vendor-neutral information about best practices in the management and stewardship
of enterprise information, has defined data governance as "a system of
decision rights and accountabilities for information-related processes,
executed according to agreed-upon models which describe who can take what
actions with what information, and when, under what circumstances, using what
methods.”

And, the experts all agree that MDM initiatives that lack formal
data governance policies have a higher likelihood of failure. Why?Because data governance not only helps
to ensure the integrity of the master data that stakeholders use to formulate
important business plans and make critical day-to-day business decisions, it
aids in effective compliance with regulatory and information disclosure
demands.

However, Gartner predicts that 90 percent of organizations will
not succeed at their first attempts at data governance.This failure can be caused by a variety
of common factors, including:

Too much reliance on IT.According to Ventana Research’s
Mark Smith, responsibility for data quality is not just IT’s job.It is up to information consumers
within functional business units – who have insight into the context in
which master data is used – to help administer these assets.

No clear documentation.Data governance policies and
related procedures must be defined and documented in a way that both
technical and business stakeholders can easily understand, and must be
readily accessible to all those who generate or interact with master
data.

Poor enforcement. Data governance
processes that are loosely enforced – or not enforced at all – are not
likely to be adhered to.Documentation must not only account for what the rules and
guidelines are, but what the possible penalties will be if they are not
properly followed.

In some scenarios, bad or invalid master data may be worse than no
master data at all.In order to
preserve the correctness and consistency of master data across an organization,
companies must implement a formalized data governance program that includes
strict “checks and balances” that are overseen by a council of key stakeholders
from both the IT team, and various business units.Only then can master data be optimized to ensure accuracy,
comprehensiveness, and most importantly, relevance to all those who rely on it
to support core business activities.

To learn more about best practices in data governance and master
data management, visit the Croyten Web site at www.croyten.com.

October 07, 2007

When the subject of metadata arises, and the decision to develop and implement a metadata management strategy is made, companies tend look at the subject matter from only a technical perspective.The most common, yet incorrect, assumption is that the primary goal of a metadata management initiative is to simplify and enhance the way IT staff control, administrate, and govern enterprise information assets.

While that aspect is, indeed, very important, it is only a piece of the puzzle.In order for an organization to derive maximum value from its metadata management strategy, it must ensure that metadata has not only technical value, but true and tangible business value as well.Therefore, it will need to create, collect, and store its metadata in a way that can be fully leveraged by IT staff, as well as functional users across the business.

Knowledge workers require metadata for two main reasons.First, metadata enhances a variety of mission-critical corporate activities, such as business intelligence and analysis, financial reporting, and other operations that require the consumption of the most timely and accurate business data possible.Second, while the technical aspects of metadata management focus on the data itself, such as where it resides and who has authorization to access it, business users are more concerned with information validation, consistency, and integrity.They need to know that the information they rely on to perform their jobs was collected, stored, and processed in the right manner.

For example, a sales manager will want to know if the numbers in the sales forecasts or commission reports he or she generates are correct.Financial managers will want a complete audit trail for revenue numbers, so they know that the information in their quarterly reports was gathered and archived according to regulatory standards.And, plant managers will want to make sure that product defect and inventory data is completely accurate and up-to-date before they conduct their analysis.

But raw metadata is quite technical in nature, and can seem almost cryptic to the average business person.Additionally, getting functional users to learn and utilize the traditional metadata tools, which are very technically-oriented and not particularly intuitive, may be rather challenging.

So, in order for a metadata management initiative to deliver the kind of return on investment that companies expect, metadata must be structured and optimized in a way that is accessible, relevant, and usable to non-technical employees at all levels.The best way to ensure this is to follow these simple steps:

Create a board of functional users, perhaps by choosing one representative from every major department or division, and have the members serve as advisors who can educate IT staff about how business users need to access and consume metadata

Ensure that those business users are closely involved in the project from start to finish, so every phase of the project takes functional needs into account

Align metadata strategies with core business goals, objectives, and processes as part of the initial planning process

Deploy tools, procedures, and techniques that enable metadata to be rapidly transformed into intelligent, easy-to-understand business terms

After implementation, closely monitor usage by knowledge workers, and continuously tweak and enhance the environment as needed to improve metadata usability and value

September 20, 2007

Like most companies today, your organization has probably deployed countless software applications, database systems, data warehouses, and other technology components across your business.These disparate environments, while unavoidable, make the enterprise-wide management of data more challenging than ever before.

The creation and use of metadata – descriptive details about the data in your enterprise – is one of the most popular and effective ways to achieve enhanced control and management over your information assets.There are several different types of metadata that reside in a variety of locations across an organization, including relational databases, unstructured documents and graphic files, and data warehouses and business intelligence applications.

By collecting, storing, tracking, and analyzing that metadata, organizations can realize significant benefits, achieving a greater understanding of their data assets, their purpose, and their utilization.As a result, a successful metadata management strategy can facilitate:

Closer alignment of information resources with key strategies and goals

Metadata management also allows for enhanced compliance with Sarbanes Oxley, BASEL II, and other regulatory reporting guidelines.It ensures appropriate storage, retention, and disposal of mission-critical financial information – including that contained in unstructured formats such as emails and images.Additionally, it creates a virtual “audit trail”, such as what systems key accounting data resides in, how is it aggregated from various databases, or how expense, revenue, and profit numbers are calculated.

But, knowing the value it can deliver, how does an organization begin the process of generating, gathering, and leveraging metadata to create enterprise-wide advantages? In order to successfully plan and implement an enterprise-wide metadata management strategy, a company must follow these steps:

Clearly define needs by working closely with both functional and technical stakeholders

Prioritize key requirements

Map out an infrastructure to meet those needs, including the tools that will support the initiative

There are a wide range of metadata management tools to choose from.And because technology environments and data management needs vary greatly from one business to the next, there is no single solution or combination of solutions that is ideal for every company.Data dictionaries; computer aided systems engineering (CASE) and application design solutions; extract, transform, and load (ETL) tools; metadata repositories; and administration services all serve as the “building blocks” of the architectures that enable efficient metadata management across an enterprise.Each organization must select appropriate tools, based on its unique and specific needs.

It is also very important to understand that metadata management is about more than just technologies.Advanced methodologies and practices must be combined with your technology strategy to foster a greater understanding of where key data elements are located, what their primary function is, how users access them, and what interdependencies exist.The development and documentation of formal procedures for the collection, storage, and use of metadata, as well as the creation of rigid metadata governance policies and the assignment of “owners” to oversee those rules, are also critical to effective metadata management.

In future posts, I will discuss, in greater detail, some of the various aspects of successful data management strategies and solutions.